Images produced by cameras on mobile electronic devices such as cell phones and personal digital assistants (PDAs) are often of poor quality because cameras of the mobile electronic devices have inexpensive optics, small apertures, slow shutters and in many cases fixed-focus lenses. Such cameras often show blur (both motion and focus blur) and noise. Moreover, in situations where the lenses are close to the object, the depth of field is poor and the blur problem grows worse with the introduction of varying amounts of blur through the images produced by different lenses. Illumination variations are an additional problem and cannot be rectified easily using the flash on cell phone cameras, since the flash on these devices is usually not strong enough and tends to create illumination variations.
Some efforts have been made to identify objects captured in images produced by cameras of poor quality. However, conventional systems for detecting objects captured by such images are often complex and require extensive computation by the processor of the camera device. One type of conventional system uses a number of filters that each analyze and process the image for objects associated with that filter. However, such system utilizes a large amount of processing power. There is, therefore, a need for a robust and efficient method and system for detecting predetermined objects in images of varying quality produced by mobile electronic devices such as cell phones and PDAs.